The recent advancements in privacy-preserving machine learning have shown significant progress, particularly in the application of differential privacy (DP) to various models and algorithms. A notable trend is the extension of DP techniques to more complex and high-dimensional data settings, such as spatial distributions and image classification, where traditional DP methods often fall short in maintaining both privacy and utility. Innovations in DP mechanisms, such as the Disk Area Mechanism for spatial data and the use of binning for streaming data, have demonstrated improved accuracy and efficiency, addressing the need for scalable and robust privacy-preserving solutions. Additionally, the integration of DP with adversarial robustness and fairness considerations has emerged as a critical area, aiming to mitigate biases and disparate impacts that can arise from DP-protected models. These developments collectively push the boundaries of what is possible in privacy-preserving data analysis, offering new tools and insights for practitioners in sensitive data domains.